Workshop 11 | Machine Learning: Zero to Hero

The objective of the support vector machine algorithm is to find a hyperplane in N-dimensional space(N — the number of features) that distinctly classify the data points. Now how it actually works? SVM in the real-world has been used to classify images and for facial recognition, if we take ourselves back in the 1990s to 2004. Thanks to Neural Networks, we can do bigger things. But just for a record, learning SVM is fun. SVM is still pretty much involved in the lives of Data Scientists and AI Engineers. After all Classical never leaves you 😁

We have already seen Support Vector Machine in-depth in Regression but this time we will fancy ourselves and dive into this Classification Mystery. One of the most important algorithms in the world of Machine Learning.